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A portable wireless device for cyclic alternating pattern estimation from an EEG monopolar derivation

2019Journal article Open access
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dc.contributor.authorMendonça, Fabio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.contributor.authorRavelo-García, Antonio G.
dc.date.accessioned2024-02-14T14:33:34Z
dc.date.available2024-02-14T14:33:34Z
dc.date.issued2019
dc.description.abstractQuality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMendonça, F., Mostafa, S. S., Morgado-Dias, F., & Ravelo-García, A. G. (2019). A portable wireless device for cyclic alternating pattern estimation from an EEG monopolar derivation. Entropy, 21(12), 1203.pt_PT
dc.identifier.doi10.3390/e21121203pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5550
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory for Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSleep qualitypt_PT
dc.subjectEEGpt_PT
dc.subjectCAPpt_PT
dc.subjectGRUpt_PT
dc.subjectLSTMpt_PT
dc.subject1D-CNNpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleA portable wireless device for cyclic alternating pattern estimation from an EEG monopolar derivationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory for Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50009%2F2019/PT
oaire.citation.issue12pt_PT
oaire.citation.startPage1203pt_PT
oaire.citation.titleEntropypt_PT
oaire.citation.volume21pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationf90aafd0-eedb-47ea-945a-40b1c1fe802a
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relation.isAuthorOfPublication.latestForDiscovery042f7593-c6ca-4553-8f0e-12ccf17018be
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